In [1]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import time
import pickle
import sklearn.svm as svm
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.externals import joblib
1. Make Vehicle Train, Test, Validation Dataset¶
In [2]:
# Loading + training,validation test split
cars0 = glob.glob('./vehicleDB/GTI/vehicles/Far/*.png')
cars1 = glob.glob('./vehicleDB/GTI/vehicles/MiddleClose/*.png')
cars2 = glob.glob('./vehicleDB/GTI/vehicles/Left/*.png')
cars3 = glob.glob('./vehicleDB/GTI/vehicles/Right/*.png')
#cars4 = glob.glob('./vehicles/KITTI_extracted/*.png')
#cars4 += glob.glob('./vehicles/augmentation/*.png')
notcars1 = glob.glob('./vehicleDB/GTI/non-vehicles/Far/*.png')
notcars1 += glob.glob('./vehicleDB/GTI/non-vehicles/MiddleClose/*.png')
notcars1 += glob.glob('./vehicleDB/GTI/non-vehicles/Left/*.png')
notcars1 += glob.glob('./vehicleDB/GTI/non-vehicles/Right/*.png')
# split 70% training 20% validation 10% test set
frac1 = 0.7
l0, l1, l2, l3, l5=len(cars0), len(cars1), len(cars2), len(cars3), len(notcars1)
L1 = (frac1*np.array([l0, l1, l2, l3, l5])).astype('int')
frac2 = 0.9
l0, l1, l2, l3, l5=len(cars0), len(cars1), len(cars2), len(cars3), len(notcars1)
L2 = (frac2*np.array([l0, l1, l2, l3, l5])).astype('int')
cars_train = cars0[:L1[0]] + cars1[:L1[1]] + cars2[:L1[2]] + cars3[:L1[3]]
notcars_train = notcars1[:L1[4]]
cars_val = cars0[L1[0]:L2[0]] + cars1[L1[1]:L2[1]] + cars2[L1[2]:L2[2]] + cars3[L1[3]:L2[3]]
notcars_val = notcars1[L1[4]:L2[4]]
cars_test = cars0[L2[0]:] + cars1[L2[1]:] + cars2[L2[2]:] + cars3[L2[3]:]
notcars_test = notcars1[L2[4]:]
In [3]:
L1,L2
Out[3]:
In [4]:
print('Number of samples in cars training set: ', len(cars_train))
print('Number of samples in notcars training set: ', len(notcars_train))
print('Number of samples in cars validation set: ', len(cars_val))
print('Number of samples in notcars validation set: ', len(notcars_val))
print('Number of samples in cars test set: ',len(cars_test))
print('Number of samples in notcars test set: ',len(notcars_test))
Save Dataset¶
In [5]:
# Save the data for easy access
pickle_file = './trained_weight/datasets.p'
print('Saving data to pickle file...')
try:
with open(pickle_file, 'wb') as pfile:
pickle.dump(
{
'cars_train': cars_train,
'notcars_train': notcars_train,
'cars_val': cars_val,
'notcars_val': notcars_val,
'cars_test': cars_test,
'notcars_test': notcars_test
},
pfile, pickle.HIGHEST_PROTOCOL)
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
print('Data cached in pickle file.')
Open Datasets and Display vehicle/non-vehicle¶
In [6]:
data_file = './trained_weight/datasets.p'
with open(data_file, mode='rb') as f:
data = pickle.load(f)
cars_train = data['cars_train']
notcars_train = data['notcars_train']
cars_val = data['cars_val']
notcars_val = data['notcars_val']
cars_test = data['cars_test']
notcars_test = data['notcars_test']
In [7]:
i=22
a_car = plt.imread(cars_train[i])
not_a_car = plt.imread(notcars_train[i])
cars_train[i],notcars_train[i]
Out[7]:
In [8]:
font_size=30
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(a_car)
ax1.set_title('Vehicle', fontsize=font_size)
ax2.imshow(not_a_car)
ax2.set_title('Non-Vehicle', fontsize=font_size)
plt.rc('xtick', labelsize=font_size)
plt.rc('ytick', labelsize=font_size)
plt.show()
2. Train HOG and SVM¶
In [9]:
import matplotlib.image as mpimg
import numpy as np
import cv2
from skimage.feature import hog
In [10]:
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
In [11]:
# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(img, size).ravel()
# Return the feature vector
return features
In [12]:
# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
In [13]:
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
In [14]:
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_windows = np.int(xspan/nx_pix_per_step) - 1
ny_windows = np.int(yspan/ny_pix_per_step) - 1
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
In [15]:
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
In [16]:
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
return np.concatenate(img_features)
In [17]:
# Define a function you will pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
Load the training validation and test data¶
In [18]:
data_file = './trained_weight/datasets.p'
with open(data_file, mode='rb') as f:
data = pickle.load(f)
cars_train = data['cars_train']
cars_val = data['cars_val']
cars_test = data['cars_test']
notcars_train = data['notcars_train']
notcars_val = data['notcars_val']
notcars_test = data['notcars_test']
In [19]:
#helper function to extract features from files
def get_features(files, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
features = []
for file in files:
img = mpimg.imread(file)
img_features = single_img_features(img, color_space=color_space, spatial_size=spatial_size,
hist_bins=hist_bins, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
features.append(img_features)
return features
In [20]:
color_space = 'HLS' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
spatial_size = (16, 16)
hist_bins = 32
orient = 9
pix_per_cell = 8
cell_per_block = 2
hog_channel = 'ALL'
spatial_feat = True
hist_feat = True
hog_feat = True
t=time.time()
cars_train_feat = get_features(cars_train,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
cars_val_feat = get_features(cars_val,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
cars_test_feat = get_features(cars_test,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
notcars_train_feat = get_features(notcars_train,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
notcars_val_feat = get_features(notcars_val,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
notcars_test_feat = get_features(notcars_test,color_space, spatial_size,hist_bins, orient,
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG,spatial and color features...')
In [21]:
# Create an array stack of feature vectors
X = np.vstack((cars_train_feat,cars_val_feat,cars_test_feat,
notcars_train_feat,notcars_val_feat,notcars_test_feat)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
In [22]:
cars_ntrain=len(cars_train_feat)
cars_nval=len(cars_val_feat)
cars_ntest=len(cars_test_feat)
ncars_ntrain=len(notcars_train_feat)
ncars_nval=len(notcars_val_feat)
ncars_ntest=len(notcars_test_feat)
i1 = cars_ntrain
i2 = i1 + cars_nval
i3 = i2 + cars_ntest
i4 = i3 + ncars_ntrain
i5 = i4 + ncars_nval
cars_train_feat,cars_val_feat,cars_test_feat = scaled_X[:i1],scaled_X[i1:i2],scaled_X[i2:i3]
notcars_train_feat,notcars_val_feat,notcars_test_feat = scaled_X[i3:i4],scaled_X[i4:i5],scaled_X[i5:]
y_train = np.hstack((np.ones(cars_ntrain), np.zeros(ncars_ntrain)))
y_val = np.hstack((np.ones(cars_nval), np.zeros(ncars_nval)))
y_test = np.hstack((np.ones(cars_ntest), np.zeros(ncars_ntest)))
X_train = np.vstack((scaled_X[:i1],scaled_X[i3:i4]))
X_val = np.vstack((scaled_X[i1:i2],scaled_X[i4:i5]))
X_test = np.vstack((scaled_X[i2:i3],scaled_X[i5:]))
X_train,y_train = shuffle(X_train,y_train,random_state=42)
X_val,y_val = shuffle(X_val,y_val,random_state=42)
X_test,y_test = shuffle(X_test,y_test,random_state=42)
In [23]:
print('Using:',orient,'orientations',pix_per_cell,'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# use of the rbf kernel improves the accuracy by about another percent,
# but increases the prediction time up to 1.7s(!) for 100 labels. Too slow.
#svc = svm.SVC(kernel='rbf')
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Validation Accuracy of SVC = ', round(svc.score(X_val, y_val), 4))
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 100
print('My SVC predicts: ', svc.predict(X_val[0:n_predict]))
print('For these',n_predict, 'labels: ', y_val[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
In [24]:
font_size=15
f, axarr = plt.subplots(4, 7,figsize=(20,10))
f.subplots_adjust(hspace=0.2, wspace=0.05)
colorspace = cv2.COLOR_RGB2HLS
#colorspace=cv2.COLOR_RGB2HSV
#colorspace=cv2.COLOR_RGB2YCrCb
#i1,i2=22,4000
i1 = 0
i2 = len(cars_train)-1
#print(i2)
for ind,j in enumerate([i1,i2]):
#print(ind)
#print(j)
#print(cars_train[j])
image = plt.imread(cars_train[j])
feature_image = cv2.cvtColor(image, colorspace)
axarr[ind,0].imshow(image)
axarr[ind,0].set_xticks([])
axarr[ind,0].set_yticks([])
title = "car {0}".format(j)
axarr[ind,0].set_title(title, fontsize=font_size)
for channel in range(3):
axarr[ind,channel+1].imshow(feature_image[:,:,channel],cmap='gray')
title = "ch {0}".format(channel)
axarr[ind,channel+1].set_title(title, fontsize=font_size)
axarr[ind,channel+1].set_xticks([])
axarr[ind,channel+1].set_yticks([])
for channel in range(3):
features,hog_image = get_hog_features(feature_image[:,:,channel], orient, pix_per_cell,
cell_per_block, vis=True, feature_vec=True)
axarr[ind,channel+4].imshow(hog_image,cmap='gray')
title = "HOG ch {0}".format(channel)
axarr[ind,channel+4].set_title(title, fontsize=font_size)
axarr[ind,channel+4].set_xticks([])
axarr[ind,channel+4].set_yticks([])
for indn,j in enumerate([i1,i2]):
ind=indn+2
image = plt.imread(notcars_train[j])
feature_image = cv2.cvtColor(image, colorspace)
axarr[ind,0].imshow(image)
axarr[ind,0].set_xticks([])
axarr[ind,0].set_yticks([])
title = "not car {0}".format(j)
axarr[ind,0].set_title(title, fontsize=font_size)
for channel in range(3):
axarr[ind,channel+1].imshow(feature_image[:,:,channel],cmap='gray')
title = "ch {0}".format(channel)
axarr[ind,channel+1].set_title(title, fontsize=font_size)
axarr[ind,channel+1].set_xticks([])
axarr[ind,channel+1].set_yticks([])
for channel in range(3):
features,hog_image = get_hog_features(feature_image[:,:,channel], orient, pix_per_cell,
cell_per_block, vis=True, feature_vec=True)
axarr[ind,channel+4].imshow(hog_image,cmap='gray')
title = "HOG ch {0}".format(channel)
axarr[ind,channel+4].set_title(title, fontsize=font_size)
axarr[ind,channel+4].set_xticks([])
axarr[ind,channel+4].set_yticks([])
plt.show()
#plt.savefig('./images/HOG_features_HLS.png')
#plt.savefig('./images/HOG_features_YCrCb.png')
In [25]:
# plot false positives/negatives
font_size=15
preds = svc.predict(cars_val_feat)
misclassifieds = np.array(preds != np.ones(cars_nval))
inds = np.where(preds != np.ones(cars_nval))
inds = np.ravel(inds)
misclassifieds = [ cars_val[i] for i in inds]
print('number of misclassified car images',len(misclassifieds))
for i in range(len(inds)):
print('misclassified car image ('+str(i)+') : '+misclassifieds[i])
result = plt.imread(misclassifieds[i])
plt.imshow(result)
plt.show()
#plt.savefig('./images/false_negatives.png')
In [26]:
preds = svc.predict(notcars_val_feat)
inds = np.where(preds != np.zeros(ncars_nval))
inds = np.ravel(inds)
misclassifieds = [ notcars_val[i] for i in inds]
print('number of misclassified notcar images',len(misclassifieds))
for i in range(len(inds)):
print('misclassified notcar image ('+str(i)+') : '+misclassifieds[i])
result = plt.imread(misclassifieds[i])
plt.imshow(result)
plt.show()
#plt.savefig('./images/false_positives.png')
Save Train Results¶
In [27]:
pickle_file = './trained_weight/HOG_SVM_Weight.p'
print('Saving data to pickle file...')
try:
with open(pickle_file, 'wb') as pfile:
pickle.dump(
{ 'svc':svc,
'X_scaler': X_scaler,
'color_space': color_space,
'spatial_size': spatial_size,
'hist_bins': hist_bins,
'orient': orient,
'pix_per_cell': pix_per_cell,
'cell_per_block': cell_per_block,
'hog_channel': hog_channel,
'spatial_feat': spatial_feat,
'hist_feat': hist_feat,
'hog_feat':hog_feat
},
pfile, pickle.HIGHEST_PROTOCOL)
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
print('Data cached in pickle file.')
3. Vehicle Detection by HOG and SVM¶
In [28]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import time
import pickle
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
import imageio
imageio.plugins.ffmpeg.download()
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from collections import deque
from scipy.ndimage.measurements import label
In [43]:
# Load the classifier and parameters
data_file = './trained_weight/HOG_SVM_Weight.p'
with open(data_file, mode='rb') as f:
data = pickle.load(f)
svc = data['svc']
X_scaler = data['X_scaler']
color_space = data['color_space']
spatial_size = data['spatial_size']
hist_bins = data['hist_bins']
orient = data['orient']
pix_per_cell = data['pix_per_cell']
cell_per_block = data ['cell_per_block']
hog_channel = data['hog_channel']
spatial_feat = data ['spatial_feat']
hist_feat = data['hist_feat']
hog_feat = data['hog_feat']
In [44]:
images = sorted(glob.glob('test_images/out*.png'))
images
Out[44]:
In [45]:
#image = mpimg.imread('bbox-example-image.png')
# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
#image = image.astype(np.float32)/255
def search_all_scales(image):
hot_windows = []
all_windows = []
#Y_start_stop =[[300,460]]
#XY_window = [(150,150)]
#X_start_stop =[[None,None]]
#X_start_stop =[[None,None],[None,None],[None,None]]
#Y_start_stop =[[390,440],[400,560],[400,560]]
#XY_window = [(80,80),(110,110),(130,130)]
X_start_stop =[[None,None],[None,None]]
Y_start_stop =[[390,470],[390,500]]
XY_window = [(64,64),(110,110)]
XY_overlap=[(0.75, 0.75),(0.75, 0.75)]
X_start_stop =[[None,None],[None,None],[None,None],[None,None]]
w0,w1,w2,w3 = 240,180,120,70
o0,o1,o2,o3 = 0.75,0.75,0.75,0.75
XY_window = [(w0,w0),(w1,w1),(w2,w2),(w3,w3)]
XY_overlap = [(o0,o0),(o1,o1),(o2,o2),(o3,o3)]
yi0,yi1,yi2,yi3 = 380,380,395,405
Y_start_stop =[[yi0,yi0+w0/2],[yi1,yi1+w1/2],[yi2,yi2+w2/2],[yi3,yi3+w3/2]]
for i in range(len(Y_start_stop)):
windows = slide_window(image, x_start_stop=X_start_stop[i], y_start_stop=Y_start_stop[i],
xy_window=XY_window[i], xy_overlap=XY_overlap[i])
all_windows += [windows]
hot_windows += search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
return hot_windows,all_windows
In [46]:
for file in images[1:2]:
image = mpimg.imread(file)
#image = image.astype(np.float32)/255
draw_image = np.copy(image)
t=time.time()
hot_windows,all_windows = search_all_scales(image)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to search windows ...')
#print(np.array(all_windows).shape)
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 1), thick=4)
allwindows_img = draw_image
for ind,win_list in enumerate(all_windows):
if ind==0: color= (0,0,1)
if ind==1: color= (0,1,0)
if ind==2: color= (1,0,0)
if ind==3: color= (1,1,1)
allwindows_img = draw_boxes(allwindows_img, all_windows[ind], color=color, thick=6)
plt.figure()
# Plot the result
f, (ax1,ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(window_img)
ax1.set_title('Detected windows', fontsize=40)
ax2.imshow(allwindows_img)
ax2.set_title('All windows', fontsize=40)
plt.show()
#plt.savefig('images/sliding_windows.png')
In [47]:
file0 = images[1]
file1 = images[2]
file2 = images[3]
file3 = images[4]
files = [file0,file1,file2,file3]
fig, axes = plt.subplots(2,2,figsize=(10,6))
fig.subplots_adjust(hspace=0.1, wspace=0.05)
for i, ax in enumerate(axes.flat):
image = mpimg.imread(files[i])
draw_image = np.copy(image)
hot_windows,all_windows = search_all_scales(image)
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 1), thick=4)
ax.imshow(window_img)
xlabel = "Detection example {0}".format(i)
ax.set_xlabel(xlabel)
ax.set_xticks([])
ax.set_yticks([])
plt.show()
#plt.savefig('images/detection_example.png')
Video Pipeline¶
In [48]:
# Define a class to receive the characteristics of bounding box detections
class BoundingBoxes:
def __init__(self,n=10):
# length of queue to store data
self.n = n
# hot windows of the last n images
self.recent_boxes = deque([],maxlen=n)
# current boxes
self.current_boxes = None
self.allboxes = []
def add_boxes(self):
self.recent_boxes.appendleft(self.current_boxes)
def pop_data(self):
if self.n_buffered>0:
self.recent_boxes.pop()
def set_current_boxes(self,boxes):
self.current_boxes = boxes
def get_all_boxes(self):
allboxes = []
for boxes in self.recent_boxes:
allboxes += boxes
if len(allboxes)==0:
self.allboxes = None
else:
self.allboxes = allboxes
def update(self,boxes):
self.set_current_boxes(boxes)
self.add_boxes()
self.get_all_boxes()
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
if bbox_list:
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
In [51]:
images = sorted(glob.glob('./heatmap/*.png'))
boxes = BoundingBoxes(n=6)
for file in images[0:9]:
image = mpimg.imread(file)
#image = image.astype(np.float32)/255
draw_image = np.copy(image)
t=time.time()
hot_windows,all_windows = search_all_scales(image)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to search windows ...')
boxes.update(hot_windows)
#print(np.array(all_windows).shape)
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 1), thick=4)
allwindows_img = draw_image
for ind,win_list in enumerate(all_windows):
if ind==0: color= (0,0,1)
if ind==1: color= (0,1,0)
if ind==2: color= (1,0,0)
if ind==3: color= (1,1,1)
allwindows_img = draw_boxes(allwindows_img, all_windows[ind], color=color, thick=6)
# Read in the last image shown above
heatmap = np.zeros_like(image[:,:,0]).astype(np.float)
heatmap = add_heat(heatmap, boxes.allboxes)
heatmap = apply_threshold(heatmap,5)
plt.figure()
# Plot the result
f, (ax1,ax2,ax3) = plt.subplots(1, 3, figsize=(24, 9))
f.tight_layout()
ax1.imshow(window_img)
ax1.set_title('Detected windows', fontsize=40)
ax2.imshow(allwindows_img)
ax2.set_title('All windows', fontsize=40)
ax3.imshow(heatmap)
ax3.set_title('Heatmap', fontsize=40)
plt.show()
In [52]:
labels = label(heatmap)
print(labels[1], 'cars found')
plt.imshow(labels[0], cmap='gray')
plt.show()
In [53]:
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
In [54]:
# Read in the last image above
image = mpimg.imread(images[2])
# Draw bounding boxes on a copy of the image
draw_img = draw_labeled_bboxes(image, labels)
# Display the image
plt.imshow(draw_img)
plt.show()
apply to video¶
In [55]:
boxes = BoundingBoxes(n=30)
def VehicleDetectionByHOGSVM(image):
draw_image = np.copy(image)
image = image.astype(np.float32)/255
hot_windows,_ = search_all_scales(image)
boxes.update(hot_windows)
heatmap = np.zeros_like(image[:,:,0]).astype(np.float)
heatmap = add_heat(heatmap, boxes.allboxes)
heatmap = apply_threshold(heatmap,15)
labels = label(heatmap)
window_image = draw_labeled_bboxes(draw_image, labels)
return window_image
In [56]:
project_video_output = './output_video/project_video_output_HOGSVM.mp4'
clip1 = VideoFileClip("./project_video.mp4")
In [57]:
lane_clip = clip1.fl_image(VehicleDetectionByHOGSVM) #NOTE: this function expects color images!!
%time lane_clip.write_videofile(project_video_output, audio=False)
In [58]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(project_video_output))
Out[58]:
In [ ]: